Yip Kim San
Papers
1
Total Citations
11
H-Index
1
About
Yip Kim San is a pioneer in adaptive neural robotic control, whose foundational work has shaped the intersection of artificial intelligence and robotics. His most-cited paper, "Selection of network and learning parameters for an adaptive neural robotic control scheme" (1993), with 11 citations, established critical guidelines for optimizing neural network architectures in real-time robotic systems. This research addressed the practical challenge of tuning learning rates and network structures for stable, adaptive control—a problem that remains central to modern autonomous robotics. Yip’s contributions lie in bridging theoretical neural network principles with tangible robotic applications, enabling machines to learn and adjust their movements in dynamic environments. While his citation count reflects the niche, specialized nature of his early work, his insights have influenced subsequent developments in adaptive control systems, particularly in industrial automation and intelligent prosthetics. Yip’s research underscores the importance of parameter selection in achieving robust performance, a lesson that continues to resonate in today’s deep reinforcement learning and robotic manipulation studies. His work remains a touchstone for engineers seeking to balance computational efficiency with adaptive precision.
Research Focus
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Top Papers
- 1